cplst
所属分类：人工智能/神经网络/深度学习
开发工具：matlab
文件大小：831KB
下载次数：14
上传日期：2017-11-20 10:02:50
上 传 者：
千年之后
说明： 多标签分类算法，通过对标签降维（SVD），然后利用线性回归建立特征和低维标签之间的关系，求出特征的系数，然后反过来进行预测
(Multi label classification algorithm, through the tag dimension reduction (SVD), and then use linear regression to establish the relationship between features and low dimensional tags, to find the coefficients of the feature, and then in turn to predict)
文件列表:
mlc_lsdr-c063ab24be57a22d069ca87dad463e2e739313bd\br_encode.m (129, 2017-11-18)
mlc_lsdr-c063ab24be57a22d069ca87dad463e2e739313bd\cplst_encode.m (238, 2017-11-18)
mlc_lsdr-c063ab24be57a22d069ca87dad463e2e739313bd\cssp_encode.m (681, 2015-07-15)
mlc_lsdr-c063ab24be57a22d069ca87dad463e2e739313bd\demo.m (700, 2017-11-19)
mlc_lsdr-c063ab24be57a22d069ca87dad463e2e739313bd\evaluate.m (1463, 2017-11-18)
mlc_lsdr-c063ab24be57a22d069ca87dad463e2e739313bd\FaIE_encode.m (239, 2015-07-15)
mlc_lsdr-c063ab24be57a22d069ca87dad463e2e739313bd\LICENSE (1080, 2015-07-15)
mlc_lsdr-c063ab24be57a22d069ca87dad463e2e739313bd\LSpaceTrans.m (1800, 2017-11-18)
mlc_lsdr-c063ab24be57a22d069ca87dad463e2e739313bd\plst_encode.m (210, 2017-11-18)
mlc_lsdr-c063ab24be57a22d069ca87dad463e2e739313bd\read_dataset.m (341, 2017-11-18)
mlc_lsdr-c063ab24be57a22d069ca87dad463e2e739313bd\ridgereg.m (355, 2015-07-15)
mlc_lsdr-c063ab24be57a22d069ca87dad463e2e739313bd\ridgereg_hat.m (176, 2015-07-15)
mlc_lsdr-c063ab24be57a22d069ca87dad463e2e739313bd\ridgereg_pinv.m (183, 2015-07-15)
mlc_lsdr-c063ab24be57a22d069ca87dad463e2e739313bd\round_linear_decode.m (394, 2017-11-19)
mlc_lsdr-c063ab24be57a22d069ca87dad463e2e739313bd\yeast\X_tr (2137989, 2015-07-15)
mlc_lsdr-c063ab24be57a22d069ca87dad463e2e739313bd\yeast\X_tt (237963, 2015-07-15)
mlc_lsdr-c063ab24be57a22d069ca87dad463e2e739313bd\yeast\Y_tr (297502, 2015-07-15)
mlc_lsdr-c063ab24be57a22d069ca87dad463e2e739313bd\yeast\Y_tt (33054, 2015-07-15)
mlc_lsdr-c063ab24be57a22d069ca87dad463e2e739313bd\yeast (0, 2017-11-03)
mlc_lsdr-c063ab24be57a22d069ca87dad463e2e739313bd (0, 2017-11-19)
Multi-Label Classification with Label Space Dimension Reduction
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The program consists of five linear label space transformation approaches. The base learner used in all approaches is regularized linear regression with a fixed regularization parameter. Please see the usage in demo.m .
* Binary Relevance with Random Discarding (BR), Principal Label Space Transformation (PLST) are developed in
Farbound Tai and Hsuan-Tien Lin. Multilabel classification with principal label space transformation. Neural Computation, 24(9):2508--2542, September 2012.
* Conditional Principal Label Space Transformation (CPLST) is developed in
Yao-Nan Chen and Hsuan-Tien Lin. Feature-aware label space dimension reduction for multi-label classification. In Advances in Neural Information Processing Systems: Proceedings of the 2012 Conference (NIPS), pages 1538--1546, December 2012.
* Feature-aware Implicit Label Space Encoding (FaIE) is developed in
Zijia Lin, Guiguang Ding, Mingqing Hu, and Jianmin Wang. Multi-label Classification via Feature-aware Implicit Label Space Encoding. In Proceedings of the 31st International Conference on Machine Learning (ICML), June 2014.
* Column Subset Selection Problem (CSSP) is developed in
Wei Bi and James Kwok. Efficient Multi-label Classification with Many Labels. In Proceedings of the 30th International Conference on Machine Learning (ICML), June 2013.
Please cite the these papers if you find corresponding parts of the program useful.
If there are any questions, please feel free to contact the corresponding author of the first two papers at
Hsuan-Tien Lin, htlin@csie.ntu.edu.tw
Hsuan-Tien Lin thanks his co-authors of the papers, especially Farbound Tai who contributed significantly to the initial layout of the program.
Hsuan-Tien Lin also thanks user rustle1314 on GitHub, who initiated the implementations of the FaIE and CSSP algorithms. The initial implementations are later polished by Hsuan-Tien Lin.
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